Blar i Institutt for teknisk kybernetikk på tidsskrift "Scientific Reports"
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A novel role for pigment genes in the stress response in rainbow trout (Oncorhynchus mykiss)
(Journal article; Peer reviewed, 2016)In many vertebrate species visible melanin-based pigmentation patterns correlate with high stress- and disease-resistance, but proximate mechanisms for this trait association remain enigmatic. Here we show that a missense ... -
Automated methodology for optimal selection of minimum electrode subsets for accurate EEG source estimation based on Genetic Algorithm optimization
(Peer reviewed; Journal article, 2022)High-density Electroencephalography (HD-EEG) has proven to be the EEG montage that estimates the neural activity inside the brain with highest accuracy. Multiple studies have reported the effect of electrode number on ... -
Causal connections between socioeconomic disparities and COVID-19 in the USA
(Peer reviewed; Journal article, 2022)With the increasing use of machine learning models in computational socioeconomics, the development of methods for explaining these models and understanding the causal connections is gradually gaining importance. In this ... -
Detection of Parkinson’s disease from EEG signals using discrete wavelet transform, different entropy measures, and machine learning techniques
(Peer reviewed; Journal article, 2022)Early detection of Parkinson’s disease (PD) is very important in clinical diagnosis for preventing disease development. In this study, we present efficient discrete wavelet transform (DWT)-based methods for detecting PD ... -
Intraperitoneal and subcutaneous glucagon delivery in anaesthetized pigs: effects on circulating glucagon and glucose levels
(Journal article; Peer reviewed, 2020)Glucagon is a pancreatic hormone and increases the blood glucose levels. It may be incorporated in a dual hormone artificial pancreas, a device to automatically and continuously control blood glucose levels of individuals ... -
Multi-fidelity information fusion with concatenated neural networks
(Journal article; Peer reviewed, 2022)Recently, computational modeling has shifted towards the use of statistical inference, deep learning, and other data-driven modeling frameworks. Although this shift in modeling holds promise in many applications like design ... -
Multi-objective optimization for EEG channel selection and accurate intruder detection in an EEG-based subject identification system
(Peer reviewed; Journal article, 2020)We present a four-objective optimization method for optimal electroencephalographic (EEG) channel selection to provide access to subjects with permission in a system by detecting intruders and identifying the subject. Each ... -
Synchrony and multimodality in the timing of Atlantic salmon smolt migration in two Norwegian fjords
(Peer reviewed; Journal article, 2021)The timing of the smolt migration of Atlantic salmon (Salmo salar) is a phenological trait increasingly important to the fitness of this species. Understanding when and how smolts migrate to the sea is crucial to understanding ... -
Towards a minimal EEG channel array for a biometric system using resting-state and a genetic algorithm for channel selection
(Peer reviewed; Journal article, 2020)We present a new approach for a biometric system based on electroencephalographic (EEG) signals of resting-state, that can identify a subject and reject intruders with a minimal subset of EEG channels. To select features, ... -
Two-dimensional CNN-based distinction of human emotions from EEG channels selected by Multi-Objective evolutionary algorithm
(Peer reviewed; Journal article, 2022)In this study we explore how different levels of emotional intensity (Arousal) and pleasantness (Valence) are reflected in electroencephalographic (EEG) signals. We performed the experiments on EEG data of 32 subjects from ... -
Variational multiscale reinforcement learning for discovering reduced order closure models of nonlinear spatiotemporal transport systems
(Peer reviewed; Journal article, 2022)A central challenge in the computational modeling and simulation of a multitude of science applications is to achieve robust and accurate closures for their coarse-grained representations due to underlying highly nonlinear ...